SCIGwindmodel

用DB4小波进行多尺度分解，分解级数为6层的降噪程序(DB4 Wavelets )

频谱细化分析MATLAB程序，ZOOM FFT分析，频谱放大功能( Zoom-FFT)

The study consisted of an introduction, three scans performed for each of the eleven scanners, and an adjacent interview. We utilized a within-subject design. The dependent variables were task completion time and reliability, by which we mean the share of successful scans within a task completion time below 15 seconds. The independent variables were the barcode scanners as listed in Table 1. For each participant the study was recorded on video. We also transcribed comments the participant made during the tasks.

C-C方法用来计算时间延迟和时间窗口，适用于混沌时间序列，在求混沌时间序列的相空间重构时，用来计算时间延迟。(The C-C method is used to calculate the time delay and time window,it is suitable for chaotic time series, when you restructure the phase space reconstruction for chaotic time series, you can used it to calculate the time delay.)

循环谱的估算循环互相关函数计算循环互相关(cyclic cross periodogram)

一维光子晶体带隙分析计算的matlab仿真(Matlab simulation of one-dimensional photonic band gap calculated)

对于初学者具有参考意义，是一种双隐层反向传播神经网络，最小均方误差（MMSE）的算法，通过虚拟阵元进行DOA估计，迭代自组织数据分析，FIR 底通和带通滤波器和IIR 底通和带通滤波器。( For beginners with a reference value, Is a two hidden layer back propagation neural network, Minimum mean square error (MMSE) algorithm, Conducted through virtual array DOA estimation, Iterative self-organizing data analysis, Bottom-pass and band-pass FIR and IIR filter bottom pass and band-pass filter.)

是学习PCA特征提取的很好的学习资料，本程序的性能已经超过其他算法，线性调频脉冲压缩的Matlab程序。( Is a good learning materials to learn PCA feature extraction, This program has exceeded the performance of other algorithms, LFM pulse compression of the Matlab program.)

matlab软件，生成随机数，方法BuffonMonteCarlo(matlab software generate random numbers, methods BuffonMonteCarlo)

这个例子展示了如何应用偏最小二乘回归(plsr)和主成分回归(PCR),并讨论了两种方法的有效性。 本人亲测有效(This example shows how to apply Partial Least Squares Regression (PLSR) and Principal Components Regression (PCR), and discusses the effectiveness of the two methods. PLSR and PCR are both methods to model a response variable when there are a large number of predictor variables, and those predictors are highly correlated or even collinear. Both methods construct new predictor variables, known as components,)